Investigation on matching of individualised requirements and shared manufacturing resources in the context of shared factory runtime DOI
Pulin Li, S. C. Shen, Tingting Hou

et al.

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: Nov. 22, 2024

Shared Manufacturing (SharedM) empowers individuals to engage in manufacturing via order-driven, resource-sharing processes, embodying the principles of Industry 5.0 and a human-centric approach. This study tackles challenges requirement-resource matching realise Production Planning Scheduling (PP&S), stemming from conflicts distribution ownership shared Resources (MRs). We introduce factory-level runtime framework alongside self-organising redundant algorithm based on sample average approximation efficiently manage MRs individualised orders. Then, an industrial case illustrates application modelling requirements, generation final Gantt chart. The findings demonstrate that proposed factory can align idle with personalised consumer demands effectively. paper presents viable solution for implementing factories settings, providing valuable insights into social manufacturing, SharedM, novel paradigms focused values.

Language: Английский

A disjunctive graph-based metaheuristic for flexible job-shop scheduling problems considering fixture shortages in customized manufacturing systems DOI
Jiahang Li, Qihao Liu, Cuiyu Wang

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 95, P. 102981 - 102981

Published: Feb. 20, 2025

Language: Английский

Citations

4

Leveraging digital twin into dynamic production scheduling: A review DOI

Nada Ouahabi,

Ahmed Chebak,

Oulaïd Kamach

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2024, Volume and Issue: 89, P. 102778 - 102778

Published: May 4, 2024

Language: Английский

Citations

16

A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems DOI Creative Commons

Saman Nessari,

Reza Tavakkoli‐Moghaddam, Hessam Bakhshi-Khaniki

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100485 - 100485

Published: May 29, 2024

The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays crucial role in enhancing productivity and efficiency modern manufacturing systems, aimed at optimizing the allocation of jobs to variable set machines. This paper introduces an algorithm tackle FJSSP by minimizing makespan total weighted earliness tardiness under uncertainty. hybrid effectively addresses complexities stochastic multi-objective integrating equilibrium optimizer (EO) as initial solutions generator, Non-dominated sorting genetic II (NSGA-II), simulation techniques. algorithm's effectiveness validated showcasing specific instances delivering decision results for optimal across varying levels Results reveal consistent superiority managing parameters various scales, achieving lower improved Pareto front quality compared existing methods. Particularly notable faster convergence robust performance, statistical Wilcoxon test, which confirms its reliability efficacy handling dynamic situations. These findings underscore potential providing flexible, solutions. proposed unique balance exploitative explorative capabilities within framework enables effective uncertainty FJSSP, offering flexibility customization adaptable environments.

Language: Английский

Citations

6

Dynamic-multi-task-assisted evolutionary algorithm for constrained multi-objective optimization DOI

Qianlin Ye,

Wanliang Wang, Guoqing Li

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 90, P. 101683 - 101683

Published: Aug. 10, 2024

Language: Английский

Citations

4

Digital twin-based smart shop-floor management and control: A review DOI
Cunbo Zhuang, Lei Zhang, Shimin Liu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103102 - 103102

Published: Jan. 9, 2025

Language: Английский

Citations

0

Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning DOI
Jiaxuan Shi, Fei Qiao, Juan Liu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103195 - 103195

Published: March 4, 2025

Language: Английский

Citations

0

Industrial applications of digital twins: A systematic investigation based on bibliometric analysis DOI
Jiangzhuo Ren,

Rafiq Ahmad,

Dabing Li

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103264 - 103264

Published: March 13, 2025

Language: Английский

Citations

0

Dueling double deep Q-network-based stamping resources intelligent scheduling for automobile manufacturing in cloud manufacturing environment DOI
Yanjuan Hu, Leiting Pan, Ziang Wen

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)

Published: April 15, 2025

Language: Английский

Citations

0

Virtual workflows and adaptive optimization scheduling of production process with feedback constraints DOI
Zhen Quan, Yan Wang, Xiang Liu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110728 - 110728

Published: April 16, 2025

Language: Английский

Citations

0

Digital twin driven dynamic scheduling of discrete manufacturing workshop with transportation resource constraint using multi-agent deep reinforcement learning DOI
S. Geng, Shaohua Huang, Yu Guo

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 95, P. 103042 - 103042

Published: May 1, 2025

Language: Английский

Citations

0